Abstract | ||
---|---|---|
Freeway capacity decreases at sags due to local changes in car-following behavior. Consequently, sags are often bottlenecks in freeway networks. This article presents a microscopic traffic model that reproduces traffic flow dynamics at sags. The traffic model includes a new car-following model that takes into account the influence of freeway gradient on vehicle acceleration. The face-validity of the traffic model is tested by means of a simulation study. The study site is a sag of a Japanese freeway. The simulation results are compared to empirical traffic data presented in previous studies. We show that the model is capable of reproducing the key traffic phenomena that cause the formation of congestion at sags, including the lower capacity compared to normal sections, the location of the bottleneck around the end of the vertical curve, and the capacity drop induced by congestion. Furthermore, a sensitivity analysis indicates that the traffic model is robust enough to reproduce those phenomena even if some inputs are modified to some extent. The sensitivity analysis also shows what parameters need to be calibrated more accurately for real world applications of the model. |
Year | DOI | Venue |
---|---|---|
2016 | 10.1007/s13177-014-0102-3 | International Journal of Intelligent Transportation Systems Research |
Keywords | Field | DocType |
Sag, Traffic congestion, Microscopic traffic model, Model face-validity, Sensitivity analysis | Car following,Traffic generation model,Bottleneck,Traffic flow,Simulation,Traffic model,Engineering,Traffic congestion reconstruction with Kerner's three-phase theory,Vehicle acceleration,Traffic congestion | Journal |
Volume | Issue | ISSN |
14 | 1 | 1868-8659 |
Citations | PageRank | References |
0 | 0.34 | 2 |
Authors | ||
6 |
Name | Order | Citations | PageRank |
---|---|---|---|
Bernat Goñi Ros | 1 | 1 | 0.76 |
Victor L. Knoop | 2 | 7 | 2.19 |
Yasuhiro Shiomi | 3 | 11 | 2.24 |
toshimichi takahashi | 4 | 2 | 1.50 |
Bart van Arem | 5 | 84 | 22.79 |
Serge P. Hoogendoorn | 6 | 186 | 38.38 |